A Survey on Sentiment and Emotion Analysis for Computational Literary Studies

Abstract

Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions. Emotion analysis is part of the broader and larger field of sentiment analysis, and receives increasing attention in literary studies. In the past, the affective dimension of literature was mainly studied in the context of literary hermeneutics. However, with the emergence of the research field known as Digital Humanities (DH), some studies of emotions in a literary context have taken a computational turn. Given the fact that DH is still being formed as a field, this direction of research can be rendered relatively new. In this survey, we offer an overview of the existing body of research on emotion analysis as applied to literature. The research under review deals with a variety of topics including tracking dramatic changes of a plot development, network analysis of a literary text, and understanding the emotionality of texts, among other topics.

arXiv commentPublished in ZFDG: https://zfdg.de/2019_008

References in corpus (4)

• Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter
• Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
• Analysis of a continuous-time model of structural balance
• From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales

Cited by in corpus (5)

• A Transformer-based approach to Irony and Sarcasm detection
• ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer
• Affect as a proxy for literary mood
• Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations
• Protagonists' Tagger in Literary Domain -- New Datasets and a Method for Person Entity Linkage